WikiPrint - from Polar Technologies

Overview of the meteoR package

Since the ?R language has been adopted for some key tasks in the EUPORIAS and SPECS projects (including the development of comprehensive validation and statistical-downscaling packages) a R package (meteoR) is currently under development. In the current status of this task, some functions for data exploration and access have been created. These functions allow the creation of accessible datasets from locally stored climate files, the creation of data inventories providing an overview of the characteristics of the data (variables stored, units, time resolution ...) and accessing local and remote datasets in a straightforward manner by means of simple arguments, allowing the retrieval of dimensional slices of observational, reanalysis and forecast (System4) climate data. A full R package with added capabilities (including specific plot methods) and access to new datasets will be soon released for the SPECS/EUPORIAS community, as soon as new databases are incorporated into the SPECS-EUPORIAS THREDDS Data Server and new user's needs and requirements are identified and discussed.

Vocabulary definition

In order to set a common framework with a precise definition of the variables, the meteoR package is based on the use of a vocabulary. In essence, the vocabulary is a table containing the standard names of a number of variables commonly used in impact studies and downscaling applications, subject to permanent revision or addition of new standard variables. The naming conventions and the units are based on the standard name table provided by the ?NetCDF Climate and Forecast Metadata Convention. The vocabulary consists of a table with:

"ta","temperature","degrees Celsius"
"tas","2-meter temperature","degrees Celsius"
"tasmax","maximum 2-m temperature","degrees Celsius"
"tasmin","minimum 2-m temperature","degrees Celsius"
"pr","Precipitation amount","mm"
"hus","specific_humidity","kg kg-1"
"ua","eastward_wind","m s-1"
"va","northward_wind","m s-1"


The dictionary is a table whose aim is twofold:

  1. On the one hand, the dictionary is intended for the translation of generic variables, as idiosyncratically defined in each particular dataset, to the standard variables defined in the vocabulary with their corresponding nomenclature and units. This is achieved by providing a correspondence between the name of the variable as encoded in the dataset (short_name) and the corresponding name of the standard variable as defined in the vocabulary (identifier), and by applying the corresponding transformation to the native variable in order to match the standard units by means of a scale factor and an offset. In some particular cases (e.g. the precipitation provided by the System4 model outputs), the variables are also deaccumulated.
  2. The dictionary also provides additional metadata often not explicitly declared in the datasets, regarding the time aggregation of the dataset (often referred to as the cell method). This includes the fields time_step, which is merely informative, and describes the time interval between two consecutive values, and the lower_time_bound and upper_time_bound, which are the values that should be summed to each verification time to unequivocally delimit the time span encompassed by each value.

The dictionary is a comma-sepparated text file (csv), that by default is identified with the same name than the dataset, and the extension .dic, and stored in the same directory than the dataset, although its name and location can be other if adequately specified in the loading functions by the argument dictionary. The dictionary must be created "by hand" by the user, because it requires some a priori knowledge about the characteristics of the data stored in the dataset, that can be partly obtained using the function dataInventory?. The columns of the dictionary are next described:

In the following example, we show the characteristics of the dictionary constructed for the 15 members seasonal forecast of the ECMWF's System4 model:


Note that column names matter (not so their relative order), because the data load functions will perform the conversion of the variables to the standard format by finding the corresponding values by the name of the columns. The ?meteoR package includes some dictionaries, and specific examples are given in the Examples section?